Low-complexity Channel Estimation Research Articles

Progressive Inter-Path Interference Cancellation Algorithm for Channel Estimation Using Orthogonal Time-Frequency Space.

Fractional delay-Doppler (DD) channel estimation in orthogonal time-frequency space (OTFS) systems poses a significant challenge considering the severe effects of inter-path interference (IPI). To this end, several algorithms have been extensively explored in the literature for accurate low-complexity channel estimation in both integer and fractional DD scenarios. In this work, we develop a variant of the state-of-the-art delay-Doppler inter-path interference cancellation (DDIPIC) algorithm that progressively cancels the IPI as estimates are obtained. The key advantage of the proposed approach is that it requires only a final refinement procedure reducing the complexity of the algorithm. Specifically, the time difference in latency between the proposed approach and the DDIPIC algorithm is almost proportional to the square of the number of estimated paths. Numerical results show that the proposed algorithm outperforms the other channel estimation schemes achieving lower normalized mean square error (NMSE) and bit error rate (BER).

Low-Complexity Channel Estimation for Extremely Large-Scale MIMO in Near Field

Low-Complexity Channel Estimation for Extremely Large-Scale MIMO in Near Field

Orthonormal pilot-based channel estimation with low complexity phase shift optimization and coverage enhancement for IRS-assisted B5G communication

Beyond fifth-generation (B5G) technology features millimeter wave (mmWave) to ensure low latency and high capacity while enabling multiple-input and multiple-output (MIMO) facilities. MMWave signals are attenuated over short distances, resulting in a smaller coverage area. The B5G network relies on Intelligent Reflecting Surfaces (IRS) for reducing mmWave attenuation. IRS increases mmWave coverage and reduces obstruction risks by introducing phase shifts on incident beams. Complete channel state information (CSI) is required to estimate phase shifts in IRS and precoding. A novel orthonormal pilot matrix with a low-complexity channel estimation method is proposed in this context. The proposed channel estimation method achieved maximum accuracy with limited pilot sequences. A low-complexity phase shift optimization technique is also presented in this study using estimated channel information. On the other hand, the use of the desired number of reflective elements is highlighted to maximize coverage. On the basis of received signal strength and outage probability, two approaches are presented for determining the number of reflective elements. The derived closed-form expression of outage probability (OP) seems simple. It shows close agreement with the theoretical model. Results show that the proposed model provides better achievable rates than conventional IRS models.

Windowed Information Geometry Approach for Low-Complexity Channel Estimation in Massive MIMO-OFDM Systems

Windowed Information Geometry Approach for Low-Complexity Channel Estimation in Massive MIMO-OFDM Systems

Orthogonal time-frequency space (OTFS) modulation for underwater mobile acoustic communications

This paper investigates the Orthogonal Time-Frequency Space (OTFS) Modulation for Underwater Mobile Acoustic Communications where the communication channel suffers from severe multipath and Doppler effects simultaneously. Practical OTFS modulation schemes with different parameters are designed for acoustic transmission at a center frequency of 115 kHz and a bandwidth of 11.5 kHz. The schemes are tested in lake experiments where the transmitter was anchored and the receiver was towed by a boat at a speed of approximately 1 m/s or 3.6 km/h. The receiver utilizes low-complexity channel estimation and equalization algorithms, such as NLMS (Normalized Least Mean Squares) and IPNLMS (improved proportionate NLMS) algorithms. The results show some insights of the OTFS scheme for acoustic communications. First, the mobile acoustic channel characteristics are different in the 2D delay-Doppler domain than those in the 1D time and frequency domains for the Single-Carrier Coherent Modulation (SCCM) and the Orthogonal frequency division modulation (OFDM), respectively. Second, in mobile scenarios, the OTFS scheme receiver effectively and significantly reduces the accuracy requirements of the Doppler compensation algorithm and provides better frequency-selective fading suppression and Doppler effect robustness compared to SCCM and OFDM schemes. Third, the OTFS scheme has better anti-multipath performance and reduces multipath interference by effectively differentiating signals on different paths in the 2D delay-Doppler domain.

UAV-Assisted Opportunistic Beamforming in Internet of Things Networks

Opportunistic beamforming (OBF) is a promising multiple-input-multiple-output (MIMO) downlink precoding technique, where multiuser diversity gain is achieved to deal with the performance loss caused by low-complexity channel estimation. Due to high performance and low-complexity channel estimation, OBF is appropriate for internet of things (IoT) networks.However, the conventional OBF cannot achieve satisfactory performance in post-disaster communication, and the low signal-to-noise ratio (SNR) makes it hard to establish communication between the base station (BS) and users. Therefore, we propose an unmanned aerial vehicle (UAV)-assisted OBF IoT network (UON) to satisfy the quality of service (QoS) requirements of users. Then, an optimization problem is formulated to maximize the weighted energy and spectrum efficiencies, while the optimization problem is non-convex and hard to solve. To obtain the solution, we divide the optimization problem into three suboptimal issues, and then a joint iterative algorithm is applied. According to numerical results, the proposed scheme achieves higher system performances compared with the conventional OBF scheme in both Rayleigh and Rician channels. Moreover, we discuss the objective functions with different weights of spectrum efficiency (SE) and energy efficiency (EE) to adapt the QoS requirements of different scenarios. It is indicated that the proposed scheme can achieve high performance with low computational complexity, regardless of the weighted energy and spectrum efficiency.

A Low-Complexity OTFS Channel Estimation Method for Fractional Delay-Doppler Scenarios

We propose a low-complexity channel estimation method for Orthogonal Time Frequency Space (OTFS) modulation based systems. Two important aspects of OTFS modulation, namely, (i) fine delay-Doppler (DD) resolution which results in path separation and, (ii) the separability of the effective DD domain channel gain expression into delay domain and Doppler domain terms, are used to propose an iterative channel estimation method which separately estimates the delay, Doppler shift and the channel gain for each channel path. The proposed estimation does not require matrix inversion and therefore has lower complexity when compared to other known methods. The proposed method is shown to achieve a normalized mean square estimation error (NMSE) performance better than other methods.

Channel Estimation for Near-Field XL-RIS-Aided mmWave Hybrid Beamforming Architectures

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has been a prospective capacity-enhancing technique. Faced with XL-RIS-aided millimeter wave (mmWave) communications, we propose a low-complexity channel estimation strategy with the near-field spherical wavefront assumption. It separates the whole channel estimation procedure into two phases to decrease pilot use and computational complexity: 1) estimation of angular-domain parameters of the base station (BS) and users equipped with the hybrid beamforming architecture, and 2) estimation of cascaded angular- and polar-domain parameters of the XL-RIS. Particularly, in the first and second phases, the three-dimensional multiple measurement vector compressive sensing (3D-M-CS) framework and the 3D distributed CS (3D-D-CS) framework are proposed for enhanced parameter estimation, respectively. Then the variants of Look Ahead Orthogonal Match Pursuit (LAOMP), i.e., 3D-M-LAOMP and 3D-D-LAOMP, are developed for solving our proposed CS frameworks.

A Low-Complexity Channel Estimation in Internet of Vehicles in Intelligent Transportation Systems for 5G Communication

The objective of utilizing mmWave/subTHz bands in next-generation wireless communications is to be achieved. Despite this, since reconfigurable intelligent surface (RIS)-assisted systems depend on the transmission channel configuration, the system architecture design, and the methods used to derive channel state information (CSI) on a base station (BS) and RIS, channel estimation continues to be the main problem with these systems. This research proposes an innovative RIS-based and compressed sensing-based channel estimation technique for the internet of vehicles. To obtain the best phase shift matrix, the communication model must first be constructed, and the angle-of-arrival and departure are utilized. Channel estimation is then performed based on the perception matrix. The training overhead and complexity of the channel estimation are reduced by considering the position information of the vehicles in the optimal phase shift matrix. Simulation results show that the proposed algorithm exhibits better channel estimation and low complexity performance compared with existing algorithms.

Doppler Shift and Channel Estimation for Intelligent Transparent Surface Assisted Communication Systems on High-Speed Railways

The critical distinction between the emerging intelligent transparent surface (ITS) and intelligent reflection surface (IRS) is that the incident signals can penetrate the ITS instead of being reflected, which enables the ITS to combat the severe signal penetration loss for high-speed railway (HSR) wireless communications. This paper thus investigates the channel estimation problem for an ITS-assisted HSR network where the ITS is embedded into the carriage window. We first formulate the channels as functions of physical parameters, and thus transform the channel estimation into a parameter recovery problem. Next, we design the first two pilot blocks within each frame and develop a serial low-complexity channel estimation algorithm. Specifically, the channel estimates are initially obtained, and each estimate is further expressed as the sum of its perfectly known value and the estimation error. By leveraging the relationship between channels for the two pilot blocks, we recover the Doppler shifts from the channel estimates, based on which we can further acquire other channel parameters. Moreover, the Cramer-Rao lower bound (CRLB) for each parameter is derived as a performance benchmark. Finally, we provide numerical results to establish the effectiveness of our proposed estimators.

Low-Complexity Channel Estimation Using Supercomplementary Blocks of Sequences

The paper analyzes channel impulse response (CIR) estimation using aperiodic transmit sequences and a receiver which removes carrier frequency offset (CFO) after correlation. It is shown that when there are guard intervals between the transmit sequences longer than the CIR, a class of sequence sets previously introduced can be applied to achieve CIR estimation without any correlation sidelobes for an arbitrary CFO. As having these guard intervals may be viewed as a waste of on-air time, the same problem is solved when they are eliminated by introducing a new class of sequence blocks. Furthermore, for binary sequence blocks, it is shown that by careful construction of the base Hadamard matrices the minimum sizes of these blocks can be halved. The price to pay are the correlation sidelobes that are minimized but generally are not zero anymore, except for zero CFO and a couple of small Hadamard matrix sizes.

Low-Complexity Channel Estimation for Massive MIMO Systems With Decentralized Baseband Processing

Low-Complexity Channel Estimation for Massive MIMO Systems With Decentralized Baseband Processing

Belief-Propagation-Based Low-Complexity Channel Estimation and Detection for Underwater Acoustic Communications With Moving Transceivers

Achieving reliable communications with low complexity is challenging for underwater acoustic communications with moving transceivers, where the time-varying channels need to be estimated and tracked accurately and data detection needs to be performed with low complexity. In this article, with the use of a superimposed training (ST) scheme, we address this challenge by developing a low-complexity channel estimation and tracking algorithm, which is then integrated with low-complexity data detection in the frequency domain. ST is used to acquire improved channel-tracking capability. Based on belief propagation, we design a message-passing-based low-complexity bidirectional channel estimation (LCE-MP) algorithm, where all computational intensive parts are handled by the fast Fourier transform (FFT) algorithm, thereby achieving very efficient implementation with logarithmic complexity. Specifically, a message-passing-based fast information collection algorithm is presented to acquire “local” channel estimates, followed by the fusion of local channel estimates to achieve a “global” estimate of the channel. It is shown that the computational complexity per channel tap is only in a logarithmic level for the channel estimation and tracking. Moreover, the ST-scheme-based LCE-MP algorithm is combined with FFT-based data detection and decoding, which are performed in an iterative manner. The overall complexity of channel estimation and data detection is in a logarithmic level, and the system delivers excellent performance thanks to the joint processing. Field experiments were carried out in Jiaozhou Bay in 2019, and the experimental results verify the effectiveness of the proposed technique.

Low-Complexity VBI-Based Channel Estimation for Massive MIMO Systems

Low-Complexity VBI-Based Channel Estimation for Massive MIMO Systems

A Low-Complexity Channel Estimation Based on a Least-Squares Algorithm in OFDM Systems

As the channel frequency responses (CFRs) at virtual pilot subcarriers are assumed to be zero, the estimated CFRs will have a leakage effect for discrete Fourier transform (DFT)-based channel estimation in OFDM systems. The CFRs at odd pilot subcarriers and even pilot subcarriers are related if the number of maximum channel delay points is smaller than or equal to half the number of pilots (including virtual pilots). According to this correlation, we propose a low-complexity least-squares (LS) method to estimate the CFRs at virtual even and odd pilot subcarriers, respectively. This will solve the problem of the leakage effect in DFT-based channel estimation. The proposed method does not need to know the statistical properties of the channel or insert extra pilots as with some estimation methods. Furthermore, although this method has less computation than the LS method, both have almost the same channel estimation efficiency in simulation. The channel estimation efficiency of our proposed method is still similar to that of the LS method, even if the number of maximum channel delay points is greater than half the number of pilots. Therefore, the proposed low-complexity method is very suitable for equalizer hardware implementation.

Low-Complexity Channel Estimation and Passive Beamforming for RIS-Assisted MIMO Systems Relying on Discrete Phase Shifts

Reconfigurable intelligent surfaces (RISs) are capable of enhancing the capacity of wireless networks at a low cost. In practical RIS-assisted communication systems, the acquisition of channel state information (CSI) and RIS reflection optimization constitute a pair of challenges. In this paper, a low-complexity channel estimation and passive beamforming design is proposed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First of all</i> , we conceive a low-complexity framework for maximizing the achievable rate of RIS-assisted multiple-input multiple-output (MIMO) systems having discrete phase shifts at each RIS element. In contrast to existing solutions, the proposed arrangement partitions the channel training stage into several phases, where the RIS reflection coefficients are pre-designed and the effective superposed channel is estimated instead of separately training the source-destination and source-RIS-destination links. Based on this, the active beamformer can be designed at low complexity and the RIS reflection optimization is performed by selecting that one from the pre-designed training set which maximizes the achievable rate. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Secondly</i> , we propose novel techniques for generating the training set of RIS reflection coefficients. The theoretical performance of the proposed scheme is analyzed and compared to the optimal RIS configuration. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Finally</i> , our simulation results demonstrate that the proposed framework is more competitive than its existing counterparts when relying on imperfect CSI, especially for rapidly time-varying channels having short channel coherence time.

Joint estimation and detection method based on turbo equalization framework and VAMP

In this letter, we consider the single-carrier frequency domain equalization (SC-FDE) system, and propose a low-complexity joint symbol detection and channel estimation algorithm based on the recently proposed vector approximate message passing (VAMP). Specifically, we leverage VAMP twice to estimate symbols and channels, respectively, in a turbo-like way. Moreover, this algorithm organically combines the gaussian mixture model (GMM), which can accurately simulate the sparse aggregation characteristics of the channel and effectively suppress inter symbol interference (ISI). The simulation results show that compared with the traditional linear minimum mean square error (LMMSE) estimation receiving algorithm and the existing generalized approximate message passing algorithm (GAMP), the designed receiving algorithm has significant advantages in channel estimation normalized mean square error (NMSE) and bit error ratio (BER) performance, where sharing the same order of complexity.

A Low-Complexity Channel Estimation for OFDM Systems Based on CIR Length Adaptation

A Low-Complexity Channel Estimation for OFDM Systems Based on CIR Length Adaptation

Low-Complexity Channel and Carrier Frequency Offset Estimation for Burst-Mode CPM Using Optimized Training Waveforms

We present algorithms of the channel and carrier frequency offset (CFO) estimation for burst-mode continuous phase modulation (CPM) transmission with optimized training waveforms, which are obtained through minimizing the Cramer-Rao bound (CRB). Wirtinger calculus, which provides a framework for complex-valued signal processing, is used to simplify the derivations of CRBs and maximum likelihood estimation. Unlike linear modulation, the training waveform is optimized not only with respect to the modulated training sequence but also the sampling delay and sampling rate due to the continuous phase of CPM waveforms. In order to reduce the computation complexity of the optimization, we derive a closed-form expression for efficient computation of the objective function. The numerical results demonstrate the significant performance improvement of the optimized waveforms compared to the training sequences of previous works. Based on that, we propose two schemes for CFO estimation. The first scheme is a suboptimal maximum likelihood estimation of the CFO and it achieves a better trade-off between the estimation accuracy and complexity compared to optimal maximum likelihood estimation. The second scheme is based on the channel estimation, which leads to much lower implementation complexity than the other estimators. Finally, the measured bit error rate reveals the considerable gain of the estimation algorithm with the optimized training waveform.

Adaptive Channel Estimation for OFDM Systems Using CIR Length Estimate

This letter addresses adaptive channel estimation for OFDM systems to reduce computational complexity. There are numerous methods suggested for low-complexity channel estimation, but some of them still suffer from a considerable complexity or performance degradation due to the channel energy leakage. We present an adaptive channel estimator based on the channel impulse response (CIR) length estimate to reduce the complexity without any performance degradation. We first introduce a simple and efficient method based on the sequential hypothesis test (SHT) to estimate the CIR length and then apply it to the adaptive implementation of the SVD-based LMMSE estimator. Simulation results demonstrate that the SHT method performs well with very low complexity and that the adaptive estimator not only reduces the complexity significantly but also provides better performance than the fixed estimators.